{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d3412dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.3)\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import datetime as timedelta\n",
    "import matplotlib.mlab as ml\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e2d4875",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def concat(*arrs) -> np.ndarray:\n",
    "    return np.concatenate(tuple(map(np.asarray, arrs)))\n",
    "\n",
    "def insert_at(outer_arr, arr, n) -> np.ndarray:\n",
    "    outer_arr = np.asarray(outer_arr)\n",
    "    prev, post = np.split(outer_arr, (n,))\n",
    "    return concat(prev, arr, post)\n",
    "\n",
    "def cross2d(x1, y1, x2, y2):\n",
    "    return x1*y2-x2*y1\n",
    "\n",
    "def is_clockwise(x1, y1, x2, y2):\n",
    "    cp = cross2d(x1, y1, x2, y2)\n",
    "    return cp < 0 if cp != 0 else None\n",
    "\n",
    "def fill_outside(x, y, ll, ur, counter_clockwise=None):\n",
    "    \"\"\"\n",
    "    Creates a polygon where x and y form a crevice of an outer\n",
    "    rectangle with lower left and upper right corners `ll` and `ur`\n",
    "    respectively. If `counter_clockwise` is `None` then the orientation\n",
    "    of the outer polygon will be guessed to be the opposite of the\n",
    "    inner connecting points.\n",
    "    \"\"\"\n",
    "    x = np.asarray(x)\n",
    "    y = np.asarray(y)\n",
    "    xmin, ymin = ll\n",
    "    xmax, ymax = ur\n",
    "    xmin, ymin = min(xmin, min(x)), min(ymin, min(y))\n",
    "    xmax, ymax = max(xmax, max(x)), max(ymax, max(y))\n",
    "    corners = np.array([\n",
    "        [xmin, ymin],\n",
    "        [xmin, ymax],\n",
    "        [xmax, ymax],\n",
    "        [xmax, ymin],\n",
    "        [xmin, ymin],\n",
    "    ])\n",
    "    lower_left = corners[0]\n",
    "    # Get closest point to splicing corner\n",
    "    x_off, y_off = x-lower_left[0], y-lower_left[1]\n",
    "    closest_n = (x_off**2+y_off**2).argmin()\n",
    "    # Guess orientation\n",
    "    p = [x_off[closest_n], y_off[closest_n]]\n",
    "    try:\n",
    "        pn = [x_off[closest_n+1], y_off[closest_n+1]]\n",
    "    except IndexError:\n",
    "        # wrap around if we're at the end of the array\n",
    "        pn = [x_off[0], y_off[0]]\n",
    "    if counter_clockwise is None:\n",
    "        counter_clockwise = not is_clockwise(*p, *pn)\n",
    "    corners = corners[::-1] if counter_clockwise else corners\n",
    "    # Join the arrays\n",
    "    corners = concat(np.array([[x[closest_n], y[closest_n]]]), corners)\n",
    "    xs, ys = np.transpose(corners)\n",
    "    return insert_at(x, xs, closest_n), insert_at(y, ys, closest_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d96750bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "DJR= pd.read_csv('DJR.csv')\n",
    "df1_N = pd.read_csv(\"df1_N.csv\")\n",
    "df2_N = pd.read_csv(\"df2_N.csv\")\n",
    "df3_N = pd.read_csv(\"df3_N.csv\")\n",
    "df4_N = pd.read_csv(\"df4_N.csv\")\n",
    "df5_N = pd.read_csv(\"df5_N.csv\")\n",
    "df6_N = pd.read_csv(\"df6_N.csv\")\n",
    "df7_N = pd.read_csv(\"df7_N.csv\")\n",
    "df8_N = pd.read_csv(\"df8_N.csv\")\n",
    "df9_N = pd.read_csv(\"df9_N.csv\")\n",
    "df10_N = pd.read_csv(\"df10_N.csv\")\n",
    "df11_N = pd.read_csv(\"df11_N.csv\")\n",
    "df12_N = pd.read_csv(\"df12_N.csv\")\n",
    "df13_N = pd.read_csv(\"df13_N.csv\")\n",
    "df14_N = pd.read_csv(\"df14_N.csv\")\n",
    "df15_N = pd.read_csv(\"df15_N.csv\")\n",
    "df16_N = pd.read_csv(\"df16_N.csv\")\n",
    "df17_N = pd.read_csv(\"df17_N.csv\")\n",
    "df18_N = pd.read_csv(\"df18_N.csv\")\n",
    "df19_N = pd.read_csv(\"df19_N.csv\")\n",
    "df20_N = pd.read_csv(\"df20_N.csv\")\n",
    "df21_N = pd.read_csv(\"df21_N.csv\")\n",
    "df22_N = pd.read_csv(\"df22_N.csv\")\n",
    "df23_N = pd.read_csv(\"df23_N.csv\")\n",
    "df24_N = pd.read_csv(\"df24_N.csv\")\n",
    "df25_N = pd.read_csv(\"df25_N.csv\")\n",
    "df26_N = pd.read_csv(\"df26_N.csv\")\n",
    "df27_N = pd.read_csv(\"df27_N.csv\")\n",
    "df28_N = pd.read_csv(\"df28_N.csv\")\n",
    "df29_N = pd.read_csv(\"df29_N.csv\")\n",
    "df30_N = pd.read_csv(\"df30_N.csv\")\n",
    "df31_N = pd.read_csv(\"df31_N.csv\")\n",
    "df32_N = pd.read_csv(\"df32_N.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1021577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 388.8x748.8 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.4,10.4))\n",
    "\n",
    "ll, ur = (129.291, 36.026), (129.2955, 36.032)\n",
    "plt.rc('font', size = 10)\n",
    "plt.rc('axes', labelsize = 10)\n",
    "plt.rc('xtick', labelsize = 10)\n",
    "plt.rc('ytick', labelsize = 10)\n",
    "plt.rc('legend', fontsize = 10)\n",
    "plt.rc('figure', titlesize = 10)\n",
    "\n",
    "\n",
    "plt.subplot(8,4,1)\n",
    "\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df1_N[\"LON\"], df1_N[\"LAT\"], c=df1_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,2)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df2_N[\"LON\"], df2_N[\"LAT\"], c=df2_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,3)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df3_N[\"LON\"], df3_N[\"LAT\"], c=df3_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,4)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df4_N[\"LON\"], df4_N[\"LAT\"], c=df4_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,5)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df5_N[\"LON\"], df5_N[\"LAT\"], c=df5_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,6)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df6_N[\"LON\"], df6_N[\"LAT\"], c=df6_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,7)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df7_N[\"LON\"], df7_N[\"LAT\"], c=df7_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,8)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df8_N[\"LON\"], df8_N[\"LAT\"], c=df8_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,9)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df9_N[\"LON\"], df9_N[\"LAT\"], c=df9_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,10)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df10_N[\"LON\"], df10_N[\"LAT\"], c=df10_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(8,4,11)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df11_N[\"LON\"], df11_N[\"LAT\"], c=df11_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,12)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df12_N[\"LON\"], df12_N[\"LAT\"], c=df12_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,13)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df13_N[\"LON\"], df13_N[\"LAT\"], c=df13_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,14)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df14_N[\"LON\"], df14_N[\"LAT\"], c=df14_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,15)\n",
    "\n",
    "#for axis in ['top', 'bottom', 'left', 'right']:\n",
    "#  plt.gca().spines[axis].set_linewidth(3)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df15_N[\"LON\"], df15_N[\"LAT\"], c=df15_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,16)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df16_N[\"LON\"], df16_N[\"LAT\"], c=df16_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,17)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df17_N[\"LON\"], df17_N[\"LAT\"], c=df17_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,18)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df18_N[\"LON\"], df18_N[\"LAT\"], c=df18_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,19)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df19_N[\"LON\"], df19_N[\"LAT\"], c=df19_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,20)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df20_N[\"LON\"], df20_N[\"LAT\"], c=df20_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,21)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df21_N[\"LON\"], df21_N[\"LAT\"], c=df21_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,22)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df22_N[\"LON\"], df22_N[\"LAT\"], c=df22_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,23)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df23_N[\"LON\"], df23_N[\"LAT\"], c=df23_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,24)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df24_N[\"LON\"], df24_N[\"LAT\"], c=df24_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,25)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df25_N[\"LON\"], df25_N[\"LAT\"], c=df25_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,26)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df26_N[\"LON\"], df26_N[\"LAT\"], c=df26_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,27)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df27_N[\"LON\"], df27_N[\"LAT\"], c=df27_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,28)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df28_N[\"LON\"], df28_N[\"LAT\"], c=df28_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,29)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df29_N[\"LON\"], df29_N[\"LAT\"], c=df29_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,30)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df30_N[\"LON\"], df30_N[\"LAT\"], c=df30_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,31)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df31_N[\"LON\"], df31_N[\"LAT\"], c=df31_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "########################################################\n",
    "########################################################\n",
    "########################################################\n",
    "plt.subplot(8,4,32)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df32_N[\"LON\"], df32_N[\"LAT\"], c=df32_N[\"NO3\"],\n",
    "            cmap='brg_r',vmin = 1, vmax =4,\n",
    "            alpha=1, s=2, marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('Figure 6.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4040b08e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 79.2x79.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(1.1,1.1))\n",
    "\n",
    "cax = ax.scatter(df15_N[\"LON\"], df15_N[\"LAT\"], c=df15_N[\"NO3\"], cmap='brg_r',vmin = 1, vmax =4, alpha=.6, s=40)\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "cbar = fig.colorbar(cax, ticks=[1,2, 3,4])\n",
    "cbar.ax.set_yticklabels(['','', '', ''])\n",
    "cbar.ax.yaxis.set_tick_params(length = 10)\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.savefig('Figure 6 colorbar.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
